Jan Trieschmann

The accurate determination of electron properties is fundamental to low-temperature plasma simulations, necessitating precise solutions to the spatially inhomogeneous electron Boltzmann equation (EBE). This work explores the use of physics-informed neural networks (PINNs) for obtaining solutions to the spatially one-dimensional EBE subject to a uniform electric field in atomic gases. Employing the two-term approximation, the resulting equation for the isotropic distribution is solved directly in kinetic energy space without the conventional transformation to total energy. This approach demonstrates the flexibility of the PINN framework in handling diverse equation formulations. To address the convergence difficulties associated with this class of kinetic equations, a new neural network architecture is introduced. It features a Fourier-feature input layer, adaptive activation functions, and a scaled multiplicative gating mechanism. It is demonstrated that this formulation preserves robust gradient flow throughout the network, which is critical for learning physically correct solutions. Benchmarking against reference data reveals that the present architecture achieves excellent agreement across both microscopic and macroscopic properties of the electrons. Furthermore, the architecture exhibits strong generalization across different gas types and a defined range of electric field strengths without requiring case-specific hyperparameter tuning. Ultimately, the excellent accuracy achieved here validates the applicability of the present PINN method.

This study examines the development of TiN/SiO_x/Cu/SiO_x/TiN memristive devices for neuromorphic applications using wedge‐type deposition and Monte Carlo simulations. Identifying critical parameters for the desired device characteristics can be challenging with conventional trial‐and‐error methods, which often obscure the effects of varying layer compositions. By employing an off‐center thermal evaporation method, a thickness gradient of SiO_x and Cu on a 4 inch wafer is created, facilitating detailed resistance map analysis through semiautomatic measurements. This approach allows for investigating the influence of layer composition and thickness while keeping other process conditions constant. Combining experimental data with simulations provides a precise understanding of layer thickness distribution and its impact on device performance. Optimizing the SiO_x layers to be below 12 nm, coupled with a discontinuous Cu layer with a nominal thickness under 0.6 nm, exhibits analog switching properties with an R_on/R_off ratio of >100, suitable for neuromorphic applications, while R × A and power exponent γ analysis show signs of multiple conduction mechanisms. The findings highlight the importance of SiO_x and Cu thickness in determining switching behavior, offering insights for developing high‐performance analog switching components for bioinspired computing systems.

Poisson’s equation plays an important role in modeling many physical systems. In electrostatic self-consistent low-temperature plasma (LTP) simulations, Poisson’s equation is solved at each simulation time step, which can amount to a significant computational cost for the entire simulation. In this paper, we describe the development of a generic machine-learned Poisson solver specifically designed for the requirements of LTP simulations in complex 2D reactor geometries on structured Cartesian grids. Here, the reactor geometries can consist of inner electrodes and dielectric materials as often found in LTP simulations. The approach leverages a hybrid CNN-transformer network architecture in combination with a weighted multiterm loss function. We train the network using highly randomized synthetic data to ensure the generalizability of the learned solver to unseen reactor geometries. The results demonstrate that the learned solver is able to produce quantitatively and qualitatively accurate solutions. Furthermore, it generalizes well on new reactor geometries such as reference geometries found in the literature. To increase the numerical accuracy of the solutions required in LTP simulations, we employ a conventional iterative solver to refine the raw predictions, especially to recover the high-frequency features not resolved by the initial prediction. With this, the proposed learned Poisson solver provides the required accuracy and is potentially faster than a pure GPU-based conventional iterative solver. This opens up new possibilities for developing a generic and high-performing learned Poisson solver for LTP systems in complex geometries.

A number of memristive devices, mainly ReRAMs, have been reported to exhibit a unique non-zero crossing hysteresis attributed to the interplay of resistive and not yet fully understood “capacitive” and “inductive” effects. This work exploits a kinetic simulation model based on a stochastic cloud-in-a-cell method to capture these effects. The model, applied to Au/BiFeO3/Pt/Ti interface-type devices, incorporates vacancy transport and capacitive contributions. The resulting nonlinear response, characterized by hysteresis, is analyzed in detail, providing an in-depth physical understanding of the virtual effects. Capacitive effects are modeled across different layers, revealing their significant role in shaping the non-zero crossing hysteresis behavior. Results from kinetic simulations demonstrate the impact of frequency-dependent impedance on the non-zero crossing phenomenon. This model provides insight into the effects of various device material properties on the non-zero crossing point, such as Schottky barrier height, device area, and oxide layer.

This feature article presents insights concerning the correlation of plasma‐enhanced chemical vapor deposition and plasma‐enhanced atomic layer deposition thin film structures with their barrier or membrane properties. While in principle similar precursor gases and processes can be applied, the adjustment of deposition parameters for different polymer substrates can lead to either an effective diffusion barrier or selective permeabilities. In both cases, the understanding of the film growth and the analysis of the pore size distribution and the pore surface chemistry is of utmost importance for the understanding of the related transport properties of small molecules. In this regard, the article presents both concepts of thin film engineering and analytical as well as theoretical approaches leading to a comprehensive description of the state of the art in this field. Perspectives of future relevant research in this area, exploiting the presented correlation of film structure and molecular transport properties, are presented.

This feature article presents recent results on the analysis of plasma/polymer interactions and the nucleation of ultra‐thin plasma films on polymeric substrates. Because of their high importance for the understanding of such processes, in situ analytical approaches of the plasma volume as well as the plasma/substrate interfaces are introduced before the findings on plasma surface chemistry. The plasma activation of polymeric substrates is divided into the understanding of fundamental processes on model substrates and the relevance of polymer surface complexity. Concerning thin film nucleation and growth, both plasma‐enhanced chemical vapor deposition and plasma‐enhanced atomic layer deposition processes as well as the combination of both processes are considered both for model substrates and technical polymers. Based on the comprehensive presentation of recent results, selective perspectives of this research field are discussed.

Low-temperature plasmas (LTPs) consist of a mixture of neutral and charged species in thermal non-equilibrium. The governing discharge physics and chemistry lead to a zoo of (excited) species interacting with bounding surfaces. Their modification (e.g., activation, etching, functionalization) is exploited in the frame of plasma processing (e.g., plasma-enhanced etching, deposition, catalysis). LTPs also render a dominant part of the many steps required in semiconductor device manufacturing. This may involve a diverse portfolio of plasma discharges, chemical precursors, surface materials, and process recipe steps to achieve desired deposit/etch patterns.^1,2 It may as well include indirect plasma discharges, for example, in the generation of extreme ultraviolet light for state of the art 13.5 nm wavelength lithography, required for device feature scales of the order of 10 nm and beyond (for instance, termed 3 nm nodes).³ Modeling and numerical simulation contribute an integral component in the design and optimization of related plasma processes. In the recent past decade, data-driven methods such as machine learning (ML), deep learning (DL), and artificial neural networks (ANNs), among other computational statistics methods, have (re-)gained enormous interest—which may be subsumed under the umbrella term artificial intelligence (AI). Although these fields have seen a renaissance and have continued to rapidly develop in the past decade, many fundamental concepts have been well established in computational statistics and related disciplines. The past and present interest in the context of plasma processing may be attributed to the potential for hidden pattern detection in correlated data and, particularly, the efficiency in related optimization tasks.^{4 – 12}

Data-driven science and technology offer transformative tools and methods to science. This review article highlights the latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS), i.e., plasma science whose progress is driven strongly by data and data analyses. Plasma is considered to be the most ubiquitous form of observable matter in the universe. Data associated with plasmas can, therefore, cover extremely large spatial and temporal scales, and often provide essential information for other scientific disciplines. Thanks to the latest technological developments, plasma experiments, observations, and computation now produce a large amount of data that can no longer be analyzed or interpreted manually. This trend now necessitates a highly sophisticated use of high-performance computers for data analyses, making artificial intelligence and machine learning vital components of DDPS. This article contains seven primary sections, in addition to the introduction and summary. Following an overview of fundamental data-driven science, five other sections cover widely studied topics of plasma science and technologies, i.e., basic plasma physics and laboratory experiments, magnetic confinement fusion, inertial confinement fusion and high-energy-density physics, space and astronomical plasmas, and plasma technologies for industrial and other applications. The final section before the summary discusses plasma-related databases that could significantly contribute to DDPS. Each primary section starts with a brief introduction to the topic, discusses the state-of-the-art developments in the use of data and/or data-scientific approaches, and presents the summary and outlook. Despite the recent impressive signs of progress, the DDPS is still in its infancy. This article attempts to offer a broad perspective on the development of this field and identify where further innovations are required.